Title: Predictive Integrative Models for the Gradual Evaluation of Diabetic Nephropathy and Retinopathy

Biography:

Samra Farid Musaed Hussein is a dedicated lecturer at the College of Applied Studies and Community Service - Health Programs, King Saud University, Riyadh, Saudi Arabia. With expertise in qualitative and quantitative analysis, academic writing, research methodology, and data collection, Samra excels in research analysis, interviewing, and article writing. Their commitment to excellence in methodology and data analysis contributes significantly to the academic community.

Abstract

In the past thirty years, the Kingdom of Saudi Arabia (KSA) has experienced a dramatic shift in its epidemiology and nutrition, leading to a widespread outbreak of non-communicable diseases and a notable rise in illness and death caused by diabetes. In addition to nephropathy, diabetic retinopathy is the most common microvascular complication of diabetes. The study investigated the correlation between diabetic retinopathy (DR) and nephropathy, considering their interconnection. Chronic elevation of blood sugar levels and widespread inflammation in the body increase the likelihood of developing diabetic retinopathy (DR) and nephropathy. Individuals with diabetes may experience concurrent renal and ocular microvascular impairment. In addition, a study carried out in Saudi Arabia on patients with diabetes found that nephropathy significantly influenced the probability of diabetic retinopathy in type 1 diabetes mellitus. Therefore, gaining knowledge and properly predicting the concurrent progression of these problems should improve the management and comprehension of diabetes' systemic influence on several organ systems. Hence, the aim of this study is to develop inclusive prognostic models that consider both diabetic retinopathy (DR) and nephropathy in individuals diagnosed with type 1 diabetes mellitus (T1DM), with the objective of improving the precision of clinical progression forecasts. This research topic encompasses several components, including multimodal data fusion, sophisticated machine learning, longitudinal analysis, and risk stratification, in order to accomplish its objective. This Integrative Predictive Models ensures that the research is thorough and has a significant impact. The Multimodal Data Fusion study aims to explore techniques for combining several forms of data, including retinal pictures, electronic health records, and genetic information, in order to provide a comprehensive dataset for the purposes of training and validating models. The study will evaluate and analyse the effectiveness of cutting-edge machine learning algorithms, including deep learning models, ensemble approaches, and explainable AI techniques, in forecasting the advancement of both diabetic retinopathy and nephropathy. Create risk stratification models to identify certain subgroups with an elevated risk of concurrent progression of diabetic retinopathy and nephropathy. This will enable the implementation of individualized interventions and treatment strategies. Longitudinal research will be done at two diabetic centers in Riyadh to capture temporal patterns and trends in disease progression, so facilitating more precise predictions over time. The patients' data will consist of demographic information and clinical features, including hypertension, Hemoglobin A1c (HbA1c), nephropathy (Serum Creatinine Levels, Estimated Glomerular Filtration Rate (eGFR), and Urinary Albumin-to-Creatinine Ratio (UACR), recorded in their electronic medical records. The retinal data is assessed by fundus photography and classified 24, Dec 2023 into five categories: No diabetic retinopathy, nonproliferative retinopathy, Mild nonproliferative retinopathy, severe nonproliferative retinopathy, and proliferative retinopathy.
Keywords: Diabetes, Diabetic Retinopathy, Nephropathy , Multimodal Data Fusion, Machine Learning, Longitudinal Analysis, Risk Stratification, Integrative Predictive Models.